scholarly journals A TBB-CUDA Implementation for Background Removal in a Video-Based Fire Detection System

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Fan Wang ◽  
Xiao Jiang ◽  
Xiao Peng Hu
Keyword(s):  
Data Transfer ◽  
Detection System ◽  
Gaussian Model ◽  
Fire Detection ◽  
Distribution Model ◽  
Memory Bandwidth ◽  
Limited Memory ◽  
Background Removal ◽  
Fire Detection System ◽  
Gaussian Distribution Model

This paper presents a parallel TBB-CUDA implementation for the acceleration of single-Gaussian distribution model, which is effective for background removal in the video-based fire detection system. In this framework, TBB mainly deals with initializing work of the estimated Gaussian model running on CPU, and CUDA performs background removal and adaption of the model running on GPU. This implementation can exploit the combined computation power of TBB-CUDA, which can be applied to the real-time environment. Over 220 video sequences are utilized in the experiments. The experimental results illustrate that TBB+CUDA can achieve a higher speedup than both TBB and CUDA. The proposed framework can effectively overcome the disadvantages of limited memory bandwidth and few execution units of CPU, and it reduces data transfer latency and memory latency between CPU and GPU.

scholarly journals Dependable Fire Detection System with Multifunctional Artificial Intelligence Framework

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 2025 ◽  
Author(s):  
Jun Hong Park ◽  
Seunggi Lee ◽  
Seongjin Yun ◽  
Hanjin Kim ◽  
Won-Tae Kim
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Transfer ◽  
Detection System ◽  
Smart Cities ◽  
Fire Detection ◽  
Machine Learning Algorithms ◽  
Detection Accuracy ◽  
Detection Systems ◽  
Fire Detection System

A fire detection system requires accurate and fast mechanisms to make the right decision in a fire situation. Since most commercial fire detection systems use a simple sensor, their fire recognition accuracy is deficient because of the limitations of the detection capability of the sensor. Existing proposals, which use rule-based algorithms or image-based machine learning can hardly adapt to the changes in the environment because of their static features. Since the legacy fire detection systems and network services do not guarantee data transfer latency, the required need for promptness is unmet. In this paper, we propose a new fire detection system with a multifunctional artificial intelligence framework and a data transfer delay minimization mechanism for the safety of smart cities. The framework includes a set of multiple machine learning algorithms and an adaptive fuzzy algorithm. In addition, Direct-MQTT based on SDN is introduced to solve the traffic concentration problems of the traditional MQTT. We verify the performance of the proposed system in terms of accuracy and delay time and found a fire detection accuracy of over 95%. The end-to-end delay, which comprises the transfer and decision delays, is reduced by an average of 72%.

scholarly journals Fire Detection System Using Machine Learning

2021 ◽  
Vol 1916 (1) ◽  
pp. 012209
Author(s):  
A Arul ◽  
R S Hari Prakaash ◽  
R Gokul Raja ◽  
V Nandhalal ◽  
N Sathish Kumar
Keyword(s):  
Machine Learning ◽  
Detection System ◽  
Fire Detection ◽  
Fire Detection System
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